Resource cache classification using machine learning

ABSTRACT

In one embodiment, techniques herein determine a plurality of resources loaded during rendering of a web page on a client device, and determine a duration of time taken for each of the plurality of resources to fully load. Accordingly, the techniques herein may then cluster the plurality of resources into clusters, comprising a first cluster consisting of resources having the shortest durations of the plurality of resources and a second cluster consisting of resources having the longest durations of the plurality of resources. Those resources of the first cluster may then be classified as cached resources, while those resources of the second cluster may be classified as non-cached resources.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to resource cache classification using machine learning.

BACKGROUND

The Internet and the World Wide Web have enabled the proliferation ofweb services available for virtually all types of businesses. Due to theaccompanying complexity of the infrastructure supporting the webservices, it is becoming increasingly difficult to maintain the highestlevel of service performance and user experience to keep up with theincrease in web services. For example, it can be challenging to piecetogether monitoring and logging data across disparate systems, tools,and layers in a network architecture. Moreover, even when data can beobtained, it is difficult to directly connect the chain of events andcause and effect.

In particular, a web browser, while rendering a web page, fetches itsresources (e.g., css, javascript, images, etc.) either over the networkor from its local cache. However, due to security concerns as well aslimitations in certain web browsers, it can be difficult to determinewhether a resource is being fetched from the browser's cache or not.Accordingly, this lack of visibility makes it correspondingly difficultto detect and manage inefficiencies in resource caching.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example computer network;

FIG. 2 illustrates an example computing device/node;

FIG. 3 illustrates an example application intelligence platform;

FIG. 4 illustrates an example system for an application-aware intrusiondetection system;

FIG. 5 illustrates an example computing system implementing thedisclosed technology;

FIG. 6 illustrates an example of resource caching;

FIG. 7 illustrates an example of resource cache classification usingmachine learning; and

FIG. 8 illustrates an example simplified procedure for resource cacheclassification using machine learning.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, techniquesherein determine a plurality of resources loaded during rendering of aweb page on a client device, and determine a duration of time taken foreach of the plurality of resources to fully load. Accordingly, thetechniques herein may then cluster the plurality of resources intoclusters, comprising a first cluster consisting of resources having theshortest durations of the plurality of resources and a second clusterconsisting of resources having the longest durations of the plurality ofresources. Those resources of the first cluster may then be classifiedas cached resources, while those resources of the second cluster may beclassified as non-cached resources.

Description

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,ranging from local area networks (LANs) to wide area networks (WANs).LANs typically connect the nodes over dedicated private communicationslinks located in the same general physical location, such as a buildingor campus. WANs, on the other hand, typically connect geographicallydispersed nodes over long-distance communications links, such as commoncarrier telephone lines, optical lightpaths, synchronous opticalnetworks (SONET), synchronous digital hierarchy (SDH) links, orPowerline Communications (PLC), and others. The Internet is an exampleof a WAN that connects disparate networks throughout the world,providing global communication between nodes on various networks. Othertypes of networks, such as field area networks (FANs), neighborhood areanetworks (NANs), personal area networks (PANs), enterprise networks,etc. may also make up the components of any given computer network.

The nodes typically communicate over the network by exchanging discreteframes or packets of data according to predefined protocols, such as theTransmission Control Protocol/Internet Protocol (TCP/IP). In thiscontext, a protocol consists of a set of rules defining how the nodesinteract with each other. Computer networks may be furtherinterconnected by an intermediate network node, such as a router, toextend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or power-line communication networks. That is, in addition toone or more sensors, each sensor device (node) in a sensor network maygenerally be equipped with a radio transceiver or other communicationport, a microcontroller, and an energy source, such as a battery.Generally, size and cost constraints on smart object nodes (e.g.,sensors) result in corresponding constraints on resources such asenergy, memory, computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations. Servers 152-154 may include,in various embodiments, any number of suitable servers or othercloud-based resources. As would be appreciated, network 100 may includeany number of local networks, data centers, cloud environments,devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc. Furthermore, in various embodiments, network 100 mayinclude one or more mesh networks, such as an Internet of Thingsnetwork. Loosely, the term “Internet of Things” or “IoT” refers touniquely identifiable objects (things) and their virtual representationsin a network-based architecture. In particular, the next frontier in theevolution of the Internet is the ability to connect more than justcomputers and communications devices, but rather the ability to connect“objects” in general, such as lights, appliances, vehicles, heating,ventilating, and air-conditioning (HVAC), windows and window shades andblinds, doors, locks, etc. The “Internet of Things” thus generallyrefers to the interconnection of objects (e.g., smart objects), such assensors and actuators, over a computer network (e.g., via IP), which maybe the public Internet or a private network.

Notably, shared-media mesh networks, such as wireless networks, areoften on what is referred to as Low-Power and Lossy Networks (LLNs),which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

FIG. 2 is a schematic block diagram of an example computing device 200that may be used with one or more embodiments described herein, e.g., asany of the devices shown in FIGS. 1A-1B above, and particularly asspecific devices as described further below. The device may comprise oneor more network interfaces 210 (e.g., wired, wireless, etc.), at leastone processor 220, and a memory 240 interconnected by a system bus 250,as well as a power supply 260 (e.g., battery, plug-in, etc.).

The network interface(s) 210 contain the mechanical, electrical, andsignaling circuitry for communicating data over links coupled to thenetwork 100, e.g., providing a data connection between device 200 andthe data network, such as the Internet. The network interfaces may beconfigured to transmit and/or receive data using a variety of differentcommunication protocols. For example, interfaces 210 may include wiredtransceivers, wireless transceivers, cellular transceivers, or the like,each to allow device 200 to communicate information to and from a remotecomputing device or server over an appropriate network. The same networkinterfaces 210 also allow communities of multiple devices 200 tointerconnect among themselves, either peer-to-peer, or up and down ahierarchy. Note, further, that the nodes may have two different types ofnetwork connections 210, e.g., wireless and wired/physical connections,and that the view herein is merely for illustration. Also, while thenetwork interface 210 is shown separately from power supply 260, fordevices using powerline communication (PLC) or Power over Ethernet(PoE), the network interface 210 may communicate through the powersupply 260, or may be an integral component of the power supply.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise hardwareelements or hardware logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242, portions ofwhich are typically resident in memory 240 and executed by theprocessor, functionally organizes the device by, among other things,invoking operations in support of software processes and/or servicesexecuting on the device. These software processes and/or services maycomprise one or more functional processes 246, and on certain devices,an illustrative “resource cache classification” process 248, asdescribed herein. Notably, functional processes 246, when executed byprocessor(s) 220, cause each particular device 200 to perform thevarious functions corresponding to the particular device's purpose andgeneral configuration. For example, a router would be configured tooperate as a router, a server would be configured to operate as aserver, an access point (or gateway) would be configured to operate asan access point (or gateway), a client device would be configured tooperate as a client device, and so on.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while the processes have been shown separately, thoseskilled in the art will appreciate that processes may be routines ormodules within other processes.

—Application Intelligence Platform—

The embodiments herein relate to an application intelligence platformfor application performance management. In one aspect, as discussed withrespect to FIGS. 3-5 below, performance within a networking environmentmay be monitored, specifically by monitoring applications and entities(e.g., transactions, tiers, nodes, and machines) in the networkingenvironment using agents installed at individual machines at theentities. For example, each node can include one or more machines thatperform part of the applications. The agents collect data associatedwith the applications of interest and associated nodes and machineswhere the applications are being operated. Examples of the collecteddata may include performance data (e.g., metrics, metadata, etc.) andtopology data (e.g., indicating relationship information). Theagent-collected data may then be provided to one or more servers orcontrollers to analyze the data.

FIG. 3 is a block diagram of an example application intelligenceplatform 300 that can implement one or more aspects of the techniquesherein. The application intelligence platform is a system that monitorsand collects metrics of performance data for an application environmentbeing monitored. At the simplest structure, the application intelligenceplatform includes one or more agents 310 and one or moreservers/controllers 320. Note that while FIG. 3 shows four agents (e.g.,Agent 1 through Agent 4) communicatively linked to a single controller,the total number of agents and controllers can vary based on a number offactors including the number of applications monitored, how distributedthe application environment is, the level of monitoring desired, thelevel of user experience desired, and so on.

The controller 320 is the central processing and administration serverfor the application intelligence platform. The controller 320 serves abrowser-based user interface (UI) 330 that is the primary interface formonitoring, analyzing, and troubleshooting the monitored environment.The controller 320 can control and manage monitoring of businesstransactions (described below) distributed over application servers.Specifically, the controller 320 can receive runtime data from agents310 (and/or other coordinator devices), associate portions of businesstransaction data, communicate with agents to configure collection ofruntime data, and provide performance data and reporting through theinterface 330. The interface 330 may be viewed as a web-based interfaceviewable by a client device 340. In some implementations, a clientdevice 340 can directly communicate with controller 320 to view aninterface for monitoring data. The controller 320 can include avisualization system 350 for displaying the reports and dashboardsrelated to the disclosed technology. In some implementations, thevisualization system 350 can be implemented in a separate machine (e.g.,a server) different from the one hosting the controller 320.

Notably, in an illustrative Software as a Service (SaaS) implementation,a controller instance 320 may be hosted remotely by a provider of theapplication intelligence platform 300. In an illustrative on-premise(On-Prem) implementation, a controller instance 320 may be installedlocally and self-administered.

The controllers 320 receive data from different agents 310 (e.g., Agents1-4) deployed to monitor applications, databases and database servers,servers, and end user clients for the monitored environment. Any of theagents 310 can be implemented as different types of agents with specificmonitoring duties. For example, application agents may be installed oneach server that hosts applications to be monitored. Instrumenting anagent adds an application agent into the runtime process of theapplication.

Database agents, for example, may be software (e.g., a Java program)installed on a machine that has network access to the monitoreddatabases and the controller. Database agents query the monitoreddatabases in order to collect metrics and pass those metrics along fordisplay in a metric browser (e.g., for database monitoring and analysiswithin databases pages of the controller's UI 330). Multiple databaseagents can report to the same controller. Additional database agents canbe implemented as backup database agents to take over for the primarydatabase agents during a failure or planned machine downtime. Theadditional database agents can run on the same machine as the primaryagents or on different machines. A database agent can be deployed ineach distinct network of the monitored environment. Multiple databaseagents can run under different user accounts on the same machine.

Standalone machine agents, on the other hand, may be standalone programs(e.g., standalone Java programs) that collect hardware-relatedperformance statistics from the servers (or other suitable devices) inthe monitored environment. The standalone machine agents can be deployedon machines that host application servers, database servers, messagingservers, Web servers, etc. A standalone machine agent has an extensiblearchitecture (e.g., designed to accommodate changes).

End user monitoring (EUM) may be performed using browser agents andmobile agents to provide performance information from the point of viewof the client, such as a web browser or a mobile native application.Through EUM, web use, mobile use, or combinations thereof (e.g., by realusers or synthetic agents) can be monitored based on the monitoringneeds. Notably, browser agents (e.g., agents 310) can include Reportersthat report monitored data to the controller.

Browser agents and mobile agents are generally unlike other monitoringthrough application agents, database agents, and standalone machineagents that are on the server. In particular, browser agents maygenerally be embodied as small files using web-based technologies, suchas JavaScript agents injected into each instrumented web page (e.g., asclose to the top as possible) as the web page is served, and areconfigured to collect data. Once the web page has completed loading, thecollected data may be bundled into a beacon and sent to an EUMprocess/cloud for processing and made ready for retrieval by thecontroller. Browser real user monitoring (Browser RUM) provides insightsinto the performance of a web application from the point of view of areal or synthetic end user. For example, Browser RUM can determine howspecific Ajax or iframe calls are slowing down page load time and howserver performance impact end user experience in aggregate or inindividual cases.

A mobile agent, on the other hand, may be a small piece of highlyperformant code that gets added to the source of the mobile application.Mobile RUM provides information on the native mobile application (e.g.,iOS or Android applications) as the end users actually use the mobileapplication. Mobile RUM provides visibility into the functioning of themobile application itself and the mobile application's interaction withthe network used and any server-side applications with which the mobileapplication communicates.

Application Intelligence Monitoring: The disclosed technology canprovide application intelligence data by monitoring an applicationenvironment that includes various services such as web applicationsserved from an application server (e.g., Java virtual machine (JVM),Internet Information Services (IIS), Hypertext Preprocessor (PHP) Webserver, etc.), databases or other data stores, and remote services suchas message queues and caches. The services in the applicationenvironment can interact in various ways to provide a set of cohesiveuser interactions with the application, such as a set of user servicesapplicable to end user customers.

Application Intelligence Modeling: Entities in the applicationenvironment (such as the JBoss service, MQSeries modules, and databases)and the services provided by the entities (such as a login transaction,service or product search, or purchase transaction) may be mapped to anapplication intelligence model. In the application intelligence model, abusiness transaction represents a particular service provided by themonitored environment. For example, in an e-commerce application,particular real-world services can include a user logging in, searchingfor items, or adding items to the cart. In a content portal, particularreal-world services can include user requests for content such assports, business, or entertainment news. In a stock trading application,particular real-world services can include operations such as receivinga stock quote, buying, or selling stocks.

Business Transactions: A business transaction representation of theparticular service provided by the monitored environment provides a viewon performance data in the context of the various tiers that participatein processing a particular request. A business transaction, which mayeach be identified by a unique business transaction identification (ID),represents the end-to-end processing path used to fulfill a servicerequest in the monitored environment (e.g., adding items to a shoppingcart, storing information in a database, purchasing an item online,etc.). Thus, a business transaction is a type of user-initiated actionin the monitored environment defined by an entry point and a processingpath across application servers, databases, and potentially many otherinfrastructure components. Each instance of a business transaction is anexecution of that transaction in response to a particular user request(e.g., a socket call, illustratively associated with the TCP layer). Abusiness transaction can be created by detecting incoming requests at anentry point and tracking the activity associated with request at theoriginating tier and across distributed components in the applicationenvironment (e.g., associating the business transaction with a 4-tupleof a source IP address, source port, destination IP address, anddestination port). A flow map can be generated for a businesstransaction that shows the touch points for the business transaction inthe application environment. In one embodiment, a specific tag may beadded to packets by application specific agents for identifying businesstransactions (e.g., a custom header field attached to an HTTP payload byan application agent, or by a network agent when an application makes aremote socket call), such that packets can be examined by network agentsto identify the business transaction identifier (ID) (e.g., a GloballyUnique Identifier (GUID) or Universally Unique Identifier (UUID)).

Performance monitoring can be oriented by business transaction to focuson the performance of the services in the application environment fromthe perspective of end users. Performance monitoring based on businesstransactions can provide information on whether a service is available(e.g., users can log in, check out, or view their data), response timesfor users, and the cause of problems when the problems occur.

A business application is the top-level container in the applicationintelligence model. A business application contains a set of relatedservices and business transactions. In some implementations, a singlebusiness application may be needed to model the environment. In someimplementations, the application intelligence model of the applicationenvironment can be divided into several business applications. Businessapplications can be organized differently based on the specifics of theapplication environment. One consideration is to organize the businessapplications in a way that reflects work teams in a particularorganization, since role-based access controls in the Controller UI areoriented by business application.

A node in the application intelligence model corresponds to a monitoredserver or JVM in the application environment. A node is the smallestunit of the modeled environment. In general, a node corresponds to anindividual application server, JVM, or Common Language Runtime (CLR) onwhich a monitoring Agent is installed. Each node identifies itself inthe application intelligence model. The Agent installed at the node isconfigured to specify the name of the node, tier, and businessapplication under which the Agent reports data to the Controller.

Business applications contain tiers, the unit in the applicationintelligence model that includes one or more nodes. Each node representsan instrumented service (such as a web application). While a node can bea distinct application in the application environment, in theapplication intelligence model, a node is a member of a tier, which,along with possibly many other tiers, make up the overall logicalbusiness application.

Tiers can be organized in the application intelligence model dependingon a mental model of the monitored application environment. For example,identical nodes can be grouped into a single tier (such as a cluster ofredundant servers). In some implementations, any set of nodes, identicalor not, can be grouped for the purpose of treating certain performancemetrics as a unit into a single tier.

The traffic in a business application flows among tiers and can bevisualized in a flow map using lines among tiers. In addition, the linesindicating the traffic flows among tiers can be annotated withperformance metrics. In the application intelligence model, there maynot be any interaction among nodes within a single tier. Also, in someimplementations, an application agent node cannot belong to more thanone tier. Similarly, a machine agent cannot belong to more than onetier. However, more than one machine agent can be installed on amachine.

A backend is a component that participates in the processing of abusiness transaction instance. A backend is not instrumented by anagent. A backend may be a web server, database, message queue, or othertype of service. The agent recognizes calls to these backend servicesfrom instrumented code (called exit calls). When a service is notinstrumented and cannot continue the transaction context of the call,the agent determines that the service is a backend component. The agentpicks up the transaction context at the response at the backend andcontinues to follow the context of the transaction from there.

Performance information is available for the backend call. For detailedtransaction analysis for the leg of a transaction processed by thebackend, the database, web service, or other application need to beinstrumented.

The application intelligence platform uses both self-learned baselinesand configurable thresholds to help identify application issues. Acomplex distributed application has a large number of performancemetrics and each metric is important in one or more contexts. In suchenvironments, it is difficult to determine the values or ranges that arenormal for a particular metric; set meaningful thresholds on which tobase and receive relevant alerts; and determine what is a “normal”metric when the application or infrastructure undergoes change. Forthese reasons, the disclosed application intelligence platform canperform anomaly detection based on dynamic baselines or thresholds.

The disclosed application intelligence platform automatically calculatesdynamic baselines for the monitored metrics, defining what is “normal”for each metric based on actual usage. The application intelligenceplatform uses these baselines to identify subsequent metrics whosevalues fall out of this normal range. Static thresholds that are tediousto set up and, in rapidly changing application environments,error-prone, are no longer needed.

The disclosed application intelligence platform can use configurablethresholds to maintain service level agreements (SLAs) and ensureoptimum performance levels for system by detecting slow, very slow, andstalled transactions. Configurable thresholds provide a flexible way toassociate the right business context with a slow request to isolate theroot cause.

In addition, health rules can be set up with conditions that use thedynamically generated baselines to trigger alerts or initiate othertypes of remedial actions when performance problems are occurring or maybe about to occur.

For example, dynamic baselines can be used to automatically establishwhat is considered normal behavior for a particular application.Policies and health rules can be used against baselines or other healthindicators for a particular application to detect and troubleshootproblems before users are affected. Health rules can be used to definemetric conditions to monitor, such as when the “average response time isfour times slower than the baseline”. The health rules can be createdand modified based on the monitored application environment.

Examples of health rules for testing business transaction performancecan include business transaction response time and business transactionerror rate. For example, health rule that tests whether the businesstransaction response time is much higher than normal can define acritical condition as the combination of an average response timegreater than the default baseline by 3 standard deviations and a loadgreater than 50 calls per minute. In some implementations, this healthrule can define a warning condition as the combination of an averageresponse time greater than the default baseline by 2 standard deviationsand a load greater than 100 calls per minute. In some implementations,the health rule that tests whether the business transaction error rateis much higher than normal can define a critical condition as thecombination of an error rate greater than the default baseline by 3standard deviations and an error rate greater than 10 errors per minuteand a load greater than 50 calls per minute. In some implementations,this health rule can define a warning condition as the combination of anerror rate greater than the default baseline by 2 standard deviationsand an error rate greater than 5 errors per minute and a load greaterthan 50 calls per minute. These are non-exhaustive and non-limitingexamples of health rules and other health rules can be defined asdesired by the user.

Policies can be configured to trigger actions when a health rule isviolated or when any event occurs. Triggered actions can includenotifications, diagnostic actions, auto-scaling capacity, runningremediation scripts.

Most of the metrics relate to the overall performance of the applicationor business transaction (e.g., load, average response time, error rate,etc.) or of the application server infrastructure (e.g., percentage CPUbusy, percentage of memory used, etc.). The Metric Browser in thecontroller UI can be used to view all of the metrics that the agentsreport to the controller.

In addition, special metrics called information points can be created toreport on how a given business (as opposed to a given application) isperforming. For example, the performance of the total revenue for acertain product or set of products can be monitored. Also, informationpoints can be used to report on how a given code is performing, forexample how many times a specific method is called and how long it istaking to execute. Moreover, extensions that use the machine agent canbe created to report user defined custom metrics. These custom metricsare base-lined and reported in the controller, just like the built-inmetrics.

All metrics can be accessed programmatically using a RepresentationalState Transfer (REST) API that returns either the JavaScript ObjectNotation (JSON) or the eXtensible Markup Language (XML) format. Also,the REST API can be used to query and manipulate the applicationenvironment.

Snapshots provide a detailed picture of a given application at a certainpoint in time. Snapshots usually include call graphs that allow thatenables drilling down to the line of code that may be causingperformance problems. The most common snapshots are transactionsnapshots.

FIG. 4 illustrates an example application intelligence platform (system)400 for performing one or more aspects of the techniques herein. Thesystem 400 in FIG. 4 includes client device 405 and 492, mobile device415, network 420, network server 425, application servers 430, 440, 450,and 460, asynchronous network machine 470, data stores 480 and 485,controller 490, and data collection server 495. The controller 490 caninclude visualization system 496 for providing displaying of the reportgenerated for performing the field name recommendations for fieldextraction as disclosed in the present disclosure. In someimplementations, the visualization system 496 can be implemented in aseparate machine (e.g., a server) different from the one hosting thecontroller 490.

Client device 405 may include network browser 410 and be implemented asa computing device, such as for example a laptop, desktop, workstation,or some other computing device. Network browser 410 may be a clientapplication for viewing content provided by an application server, suchas application server 430 via network server 425 over network 420.

Network browser 410 may include agent 412. Agent 412 may be installed onnetwork browser 410 and/or client 405 as a network browser add-on,downloading the application to the server, or in some other manner.Agent 412 may be executed to monitor network browser 410, the operatingsystem of client 405, and any other application, API, or anothercomponent of client 405. Agent 412 may determine network browsernavigation timing metrics, access browser cookies, monitor code, andtransmit data to data collection 460, controller 490, or another device.Agent 412 may perform other operations related to monitoring a requestor a network at client 405 as discussed herein including reportgenerating.

Mobile device 415 is connected to network 420 and may be implemented asa portable device suitable for sending and receiving content over anetwork, such as for example a mobile phone, smart phone, tabletcomputer, or other portable device. Both client device 405 and mobiledevice 415 may include hardware and/or software configured to access aweb service provided by network server 425.

Mobile device 415 may include network browser 417 and an agent 419.Mobile device may also include client applications and other code thatmay be monitored by agent 419. Agent 419 may reside in and/orcommunicate with network browser 417, as well as communicate with otherapplications, an operating system, APIs and other hardware and softwareon mobile device 415. Agent 419 may have similar functionality as thatdescribed herein for agent 412 on client 405, and may repot data to datacollection server 460 and/or controller 490.

Network 420 may facilitate communication of data among differentservers, devices and machines of system 400 (some connections shown withlines to network 420, some not shown). The network may be implemented asa private network, public network, intranet, the Internet, a cellularnetwork, Wi-Fi network, VoIP network, or a combination of one or more ofthese networks. The network 420 may include one or more machines such asload balance machines and other machines.

Network server 425 is connected to network 420 and may receive andprocess requests received over network 420. Network server 425 may beimplemented as one or more servers implementing a network service, andmay be implemented on the same machine as application server 430 or oneor more separate machines. When network 420 is the Internet, networkserver 425 may be implemented as a web server.

Application server 430 communicates with network server 425, applicationservers 440 and 450, and controller 490. Application server 450 may alsocommunicate with other machines and devices (not illustrated in FIG. 3).Application server 430 may host an application or portions of adistributed application. The host application 432 may be in one of manyplatforms, such as including a Java, PHP, .Net, and Node.JS, beimplemented as a Java virtual machine, or include some other host type.Application server 430 may also include one or more agents 434 (i.e.,“modules”), including a language agent, machine agent, and networkagent, and other software modules. Application server 430 may beimplemented as one server or multiple servers as illustrated in FIG. 4.

Application 432 and other software on application server 430 may beinstrumented using byte code insertion, or byte code instrumentation(BCI), to modify the object code of the application or other software.The instrumented object code may include code used to detect callsreceived by application 432, calls sent by application 432, andcommunicate with agent 434 during execution of the application. BCI mayalso be used to monitor one or more sockets of the application and/orapplication server in order to monitor the socket and capture packetscoming over the socket.

In some embodiments, server 430 may include applications and/or codeother than a virtual machine. For example, servers 430, 440, 450, and460 may each include Java code, .Net code, PHP code, Ruby code, C code,C++ or other binary code to implement applications and process requestsreceived from a remote source. References to a virtual machine withrespect to an application server are intended to be for exemplarypurposes only.

Agents 434 on application server 430 may be installed, downloaded,embedded, or otherwise provided on application server 430. For example,agents 434 may be provided in server 430 by instrumentation of objectcode, downloading the agents to the server, or in some other manner.Agent 434 may be executed to monitor application server 430, monitorcode running in a virtual machine 432 (or other program language, suchas a PHP, .Net, or C program), machine resources, network layer data,and communicate with byte instrumented code on application server 430and one or more applications on application server 430.

Each of agents 434, 444, 454, and 464 may include one or more agents,such as language agents, machine agents, and network agents. A languageagent may be a type of agent that is suitable to run on a particularhost. Examples of language agents include a Java agent, .Net agent, PHPagent, and other agents. The machine agent may collect data from aparticular machine on which it is installed. A network agent may capturenetwork information, such as data collected from a socket.

Agent 434 may detect operations such as receiving calls and sendingrequests by application server 430, resource usage, and incomingpackets. Agent 434 may receive data, process the data, for example byaggregating data into metrics, and transmit the data and/or metrics tocontroller 490. Agent 434 may perform other operations related tomonitoring applications and application server 430 as discussed herein.For example, agent 434 may identify other applications, share businesstransaction data, aggregate detected runtime data, and other operations.

An agent may operate to monitor a node, tier or nodes or other entity. Anode may be a software program or a hardware component (e.g., memory,processor, and so on). A tier of nodes may include a plurality of nodeswhich may process a similar business transaction, may be located on thesame server, may be associated with each other in some other way, or maynot be associated with each other.

A language agent may be an agent suitable to instrument or modify,collect data from, and reside on a host. The host may be a Java, PHP,.Net, Node.JS, or other type of platform. Language agent may collectflow data as well as data associated with the execution of a particularapplication. The language agent may instrument the lowest level of theapplication to gather the flow data. The flow data may indicate whichtier is communicating with which tier and on which port. In someinstances, the flow data collected from the language agent includes asource IP, a source port, a destination IP, and a destination port. Thelanguage agent may report the application data and call chain data to acontroller. The language agent may report the collected flow dataassociated with a particular application to a network agent.

A network agent may be a standalone agent that resides on the host andcollects network flow group data. The network flow group data mayinclude a source IP, destination port, destination IP, and protocolinformation for network flow received by an application on which networkagent is installed. The network agent may collect data by interceptingand performing packet capture on packets coming in from one or morenetwork interfaces (e.g., so that data generated/received by all theapplications using sockets can be intercepted). The network agent mayreceive flow data from a language agent that is associated withapplications to be monitored. For flows in the flow group data thatmatch flow data provided by the language agent, the network agent rollsup the flow data to determine metrics such as TCP throughput, TCP loss,latency, and bandwidth. The network agent may then report the metrics,flow group data, and call chain data to a controller. The network agentmay also make system calls at an application server to determine systeminformation, such as for example a host status check, a network statuscheck, socket status, and other information.

A machine agent may reside on the host and collect information regardingthe machine which implements the host. A machine agent may collect andgenerate metrics from information such as processor usage, memory usage,and other hardware information.

Each of the language agent, network agent, and machine agent may reportdata to the controller. Controller 490 may be implemented as a remoteserver that communicates with agents located on one or more servers ormachines. The controller may receive metrics, call chain data and otherdata, correlate the received data as part of a distributed transaction,and report the correlated data in the context of a distributedapplication implemented by one or more monitored applications andoccurring over one or more monitored networks. The controller mayprovide reports, one or more user interfaces, and other information fora user.

Agent 434 may create a request identifier for a request received byserver 430 (for example, a request received by a client 405 or 415associated with a user or another source). The request identifier may besent to client 405 or mobile device 415, whichever device sent therequest. In embodiments, the request identifier may be created when adata is collected and analyzed for a particular business transaction.

Each of application servers 440, 450, and 460 may include an applicationand agents. Each application may run on the corresponding applicationserver. Each of applications 442, 452, and 462 on application servers440-460 may operate similarly to application 432 and perform at least aportion of a distributed business transaction. Agents 444, 454, and 464may monitor applications 442-462, collect and process data at runtime,and communicate with controller 490. The applications 432, 442, 452, and462 may communicate with each other as part of performing a distributedtransaction. Each application may call any application or method ofanother virtual machine.

Asynchronous network machine 470 may engage in asynchronouscommunications with one or more application servers, such as applicationserver 450 and 460. For example, application server 450 may transmitseveral calls or messages to an asynchronous network machine. Ratherthan communicate back to application server 450, the asynchronousnetwork machine may process the messages and eventually provide aresponse, such as a processed message, to application server 460.Because there is no return message from the asynchronous network machineto application server 450, the communications among them areasynchronous.

Data stores 480 and 485 may each be accessed by application servers suchas application server 450. Data store 485 may also be accessed byapplication server 450. Each of data stores 480 and 485 may store data,process data, and return queries received from an application server.Each of data stores 480 and 485 may or may not include an agent.

Controller 490 may control and manage monitoring of businesstransactions distributed over application servers 430-460. In someembodiments, controller 490 may receive application data, including dataassociated with monitoring client requests at client 405 and mobiledevice 415, from data collection server 460. In some embodiments,controller 490 may receive application monitoring data and network datafrom each of agents 412, 419, 434, 444, and 454 (also referred to hereinas “application monitoring agents”). Controller 490 may associateportions of business transaction data, communicate with agents toconfigure collection of data, and provide performance data and reportingthrough an interface. The interface may be viewed as a web-basedinterface viewable by client device 492, which may be a mobile device,client device, or any other platform for viewing an interface providedby controller 490. In some embodiments, a client device 492 may directlycommunicate with controller 490 to view an interface for monitoringdata.

Client device 492 may include any computing device, including a mobiledevice or a client computer such as a desktop, work station or othercomputing device. Client computer 492 may communicate with controller390 to create and view a custom interface. In some embodiments,controller 490 provides an interface for creating and viewing the custominterface as a content page, e.g., a web page, which may be provided toand rendered through a network browser application on client device 492.

Applications 432, 442, 452, and 462 may be any of several types ofapplications. Examples of applications that may implement applications432-462 include a Java, PHP, .Net, Node.JS, and other applications.

FIG. 5 is a block diagram of a computer system 500 for implementing thepresent technology, which is a specific implementation of device 200 ofFIG. 2 above. System 500 of FIG. 5 may be implemented in the contexts ofthe likes of clients 405, 492, network server 425, servers 430, 440,450, 460, a synchronous network machine 470, and controller 490 of FIG.4. (Note that the specifically configured system 500 of FIG. 5 and thecustomized device 200 of FIG. 2 are not meant to be mutually exclusive,and the techniques herein may be performed by any suitably configuredcomputing device.)

The computing system 500 of FIG. 5 includes one or more processors 510and memory 520. Main memory 520 stores, in part, instructions and datafor execution by processor 510. Main memory 510 can store the executablecode when in operation. The system 500 of FIG. 5 further includes a massstorage device 530, portable storage medium drive(s) 540, output devices550, user input devices 560, a graphics display 570, and peripheraldevices 580.

The components shown in FIG. 5 are depicted as being connected via asingle bus 590. However, the components may be connected through one ormore data transport means. For example, processor unit 510 and mainmemory 520 may be connected via a local microprocessor bus, and the massstorage device 530, peripheral device(s) 580, portable or remote storagedevice 540, and display system 570 may be connected via one or moreinput/output (I/O) buses.

Mass storage device 530, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor unit 510. Massstorage device 530 can store the system software for implementingembodiments of the present invention for purposes of loading thatsoftware into main memory 520.

Portable storage device 540 operates in conjunction with a portablenon-volatile storage medium, such as a compact disk, digital video disk,magnetic disk, flash storage, etc. to input and output data and code toand from the computer system 500 of FIG. 5. The system software forimplementing embodiments of the present invention may be stored on sucha portable medium and input to the computer system 500 via the portablestorage device 540.

Input devices 560 provide a portion of a user interface. Input devices560 may include an alpha-numeric keypad, such as a keyboard, forinputting alpha-numeric and other information, or a pointing device,such as a mouse, a trackball, stylus, or cursor direction keys.Additionally, the system 500 as shown in FIG. 5 includes output devices550. Examples of suitable output devices include speakers, printers,network interfaces, and monitors.

Display system 570 may include a liquid crystal display (LCD) or othersuitable display device. Display system 570 receives textual andgraphical information, and processes the information for output to thedisplay device.

Peripherals 580 may include any type of computer support device to addadditional functionality to the computer system. For example, peripheraldevice(s) 580 may include a modem or a router.

The components contained in the computer system 500 of FIG. 5 caninclude a personal computer, hand held computing device, telephone,mobile computing device, workstation, server, minicomputer, mainframecomputer, or any other computing device. The computer can also includedifferent bus configurations, networked platforms, multi-processorplatforms, etc. Various operating systems can be used including Unix,Linux, Windows, Apple OS, and other suitable operating systems,including mobile versions.

When implementing a mobile device such as smart phone or tabletcomputer, the computer system 500 of FIG. 5 may include one or moreantennas, radios, and other circuitry for communicating over wirelesssignals, such as for example communication using Wi-Fi, cellular, orother wireless signals.

—Resource Cache Classification—

As noted above, a web browser, while rendering a web page, fetches itsresources (e.g., css, javascript, images, etc.) either over the networkor from its local cache. However, due to security concerns as well aslimitations in certain web browsers, it can be difficult to determinewhether a resource is being fetched from the browser's cache or not.Accordingly, this lack of visibility makes it correspondingly difficultto detect and manage inefficiencies in resource caching.

The techniques herein, therefore, propose a mechanism for resource cacheclassification using machine learning. In particular, as describedbelow, the techniques herein provide an approach (e.g., a heuristicapproach) to determine whether a browser resource is fetched from localcache or not (i.e., retrieved from a web server) for all scenarios, andto do so with a high degree of accuracy.

Specifically, according to one or more embodiments herein, techniquesherein determine a plurality of resources loaded during rendering of aweb page on a client device, and determine a duration of time taken foreach of the plurality of resources to fully load. Accordingly, thetechniques herein may then cluster the plurality of resources intoclusters, comprising a first cluster consisting of resources having theshortest durations of the plurality of resources and a second clusterconsisting of resources having the longest durations of the plurality ofresources. Those resources of the first cluster may then be classifiedas cached resources, while those resources of the second cluster may beclassified as non-cached resources. In one specific embodiment,clustering the plurality of resources into clusters further comprises athird cluster consisting of resources of the plurality of resourceshaving durations between the shortest durations and the longestdurations, and the techniques herein may thus also classify thoseresources of the third cluster as unclassified resources.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with theillustrative resource cache classification process 248, which mayinclude computer executable instructions executed by the processor 220to perform functions relating to the techniques described herein, e.g.,in conjunction with corresponding processes of other devices in thecomputer network as described herein.

Operationally, the techniques herein illustratively provide forclient-side monitoring of web page rendering, where a client agentprocess (e.g., a java script agent file) monitors page rendering,obtains performance data, and sends the end-user data to centralizedservers (e.g., controller 490) for resource cache classification. (Notethat in an alternative embodiment, the classification can also be doneat the client side, itself.)

FIG. 6 illustrates an example of resource caching in an illustrative(and simplified) environment 600, where a client device 610 has a webbrowser 612 that is configured to load and render web pages through thenetwork 620 that are served by one or more remote servers 630. As anexample, a web page may have 80-100 resources that need to be loaded andrendered (e.g., java scripts, cascading style sheets (CSS), images, adcontent, etc.). As will be understood by those skilled in the art, onceresources of a web page have been retrieved during a web page rendering,then such resources may be cached in a local cache 616 for fasterloading and rendering in the future if those same resources are usedagain. In this manner, any web page rendering by a web browser 612 mayconsist of any combination of both cached resources 640 from local cache616, or non-cached resources 650 from one or more remote servers 630.

The techniques herein illustratively use an additional feature of theapplication intelligence platform above, where a web browser agent 614(e.g., agents 412/419 above) provides a “Resource Timing API”, whichexposes details about resources loaded in a page. For instance, a webpage monitored by the application intelligence platform herein mayreport performance data (e.g., time to load, location of resources(country/region), client device, client browser, etc.) about the pageand its resources. Said differently, a web browser 612 often makes theperformance data of a page's resources available to an agent 614 throughits support of the Resource Timing API, and using the Resource TimingAPI, details about every resource's connection time, duration, size,etc., can be obtained.

As noted above, a resource may either be served from the browser's cache616 or fetched over a network 620 (servers 630). Though in certainsituations it may be determined that a resource is fetched from cache(or not) based on a file transfer size attribute (e.g., using anillustrative “transferSize” field of the Resource Timing API), manysituations do not allow for this functionality. For instance, thetransferSize field is only available in recent browser versions (since2016), so for browsers older than this (before 2016), there is no way todetermine caching using Resource Timing API. Even still, a resource caneither be a same-origin resource or a cross-origin resource. For a sameorigin resource, if transferSize is 0, then that resource is definitelycached. For a cross origin resource, however, the Resource Timing APImay not be able to provide the actual transferSize (it always reports itas zero, for security, or other reasons), and hence the transferSizefield cannot always determine if the resource was cached or not. (Theproportional contribution of cross origin resources in typical web sitescan be more than 50%).

As a part of end-user monitoring for the application intelligenceplatform, the techniques herein are aimed at bringing visibility to howeffective the browser's cache is. Accordingly, a heuristic approach isdefined herein to determine whether a resource was fetched from thecache or not, irrespective of the fact that it may be served from crossorigin servers or that the browser version is not up-to-date (e.g.,before 2016).

Using various monitoring tools, such as the illustrative Resource TimingAPI, the system herein can obtain the “duration” (time to load) of eachresource. FIG. 7 illustrates an example resource loading timeline 700(from “load start” to “load end”), showing an example duration timing ofvarious resources 705 (notably not to scale: merely an examplerepresentation for visualization). For instance, each resource mark onthe timeline 700 represents the duration/load time of that particularresource 705 during rendering of the web page. In particular, thoughresources are generally loaded in the order they are specified in theweb page, the timeline 700 of FIG. 7 represents the resources being thensorted according to their load durations, illustrating an exampleoccurrence of load time clusters.

According to the techniques herein, these durations (taken from thissingle web page rendering) are then classified into two or threeillustrative clusters using machine learning techniques (like KMeans++,Jenks Natural Breaks, Kernel Density Estimation, etc.). The firstcluster, “Cluster I”, contains the resources which have theleast/shortest durations. Conversely, a second cluster, “Cluster II”,contains resources which have the largest/longest durations. Resourcesfetched from a local cache 614 tend to have smaller duration values thatare close to each other and are very likely to form a cluster.Similarly, resources fetched over the network 620 from remote servers630 have higher duration values and tend to form the other cluster. Assuch, the techniques herein may then classify resources in Cluster I(the shorter durations) as cached resources 710 (640), while resourcesin Cluster II (the longer durations) may be classified as not beingcached (non-cached resources 720/650).

Optionally, classifying resources into three clusters achieves a higheraccuracy than only the two clusters above. In particular, the thirdcluster (“Cluster III”) contains unclassified resources 730 for which itis uncertain whether they are cached/not cached. The percentage ofresources in this cluster is often very low, and also helps inincreasing the accuracy of the solution, since otherwise the resourceswith these ambiguous durations (between the shortest and the longestdurations) may result in misclassification.

Notably, the heuristic approach herein does not depend on large trainingdata sets, and instead works on a single resource snapshot of a page.That is, using a single snapshot to do the classification avoids theneeds to create large training sets, and is based on the premise that asingle resource snapshot is the smallest volume of data that is affectedby the same environmental factors like latency, bandwidth, browser,browser version, hardware, etc.

Furthermore, according to the techniques herein, the heuristic alsodistinguishes between a first time visit (in which case very few, ifany, resources may be fetched from cache) and a repeat visit (resourcesbeing fetched from cache). That is, since during a first time visit, allresources may be remotely fetched, and as such, clustering the durationsinto groups based on duration would falsely find comparatively shorterdurations and longer durations, potentially misclassifying the shorterdurations as cached. As such, the techniques herein may define athreshold (“T”) for each browser type. If the number of resources whoseduration is <T (e.g., 20 ms) is less than a given percentage (e.g.,10%), then a page may be classified as a first time visit, and allresources may be marked as not cached. Other heuristics for determiningwhether the web page is a first time visit may be used, and this ismerely one example technique.

In certain embodiments herein, the performance data (duration, cachedvs. not cached, etc.) may be displayed on a dashboard (GUI) foradministrator assessment and adjustment. For instance, the techniquesherein can delineate which resources are cached from those that are noton a per-user basis, on a per-page basis, on a per-domain basis, on aper-browser basis, and so on. Also, the specific resources may belisted, or else various indications may be displayed (e.g., cachepercentages, max/min/average caching rates, various “health levels” orstatus indications such as color changes, size changes, and so on basedon expected cache rates, etc.). Based on this information, anadministrator, or an intelligent dynamically programming controller, canchange certain features of the web page design (e.g., changing cacheheaders) or browser design (e.g., cache size, caching algorithms, etc.)in order to achieve different cache rates.

FIG. 8 illustrates an example simplified procedure 800 for resourcecache classification using machine learning in accordance with one ormore embodiments described herein. For example, a non-generic,specifically configured device (e.g., device 200) may perform procedure800 by executing stored instructions (e.g., process 248). Specifically,when, in step 805, a web page is rendered on a client device, then theprocedure continues to step 810 where the device (e.g., controller 490)determines a plurality of resources loaded during the rendering of theweb page on a client device (e.g., both same-origin and cross-originresources), and also a duration of time taken for each of the pluralityof resources to fully load in step 815. As an illustrative example, alocal web browser agent 614 (412/419) may capture the desiredinformation (resources and/or duration), and relays it to the controller490 for further processing.

In step 820, it is important to determine whether the rendering of theweb page is the first time the web page is rendered on the clientdevice. As described above, this determination may be made in responseto determining that a given percentage of the plurality of resourceshave a duration greater than a given threshold (or, conversely, that areciprocal percentage of the plurality of resources have a duration lessthan the given threshold, thus simply referred to as “beyond athreshold”). As mentioned above, the given threshold may be specificallybased on which web browser performs the rendering of the web page.

Where it is most likely the first time a web page is being rendered onthe client device, the procedure may classify all of the plurality ofresources as non-cached resources in step 825 (i.e., as opposed totrying to cluster resource durations when in all likelihood, all or mostof the resources are actually being fetched from remote servers).

On the other hand, when the rendering is not the first time on theclient device, then in step 830 the techniques herein cluster theplurality of resources into clusters (e.g., based on a machine learningalgorithm), which as mentioned above illustratively comprise:

-   -   a first cluster consisting of resources having the shortest        durations of the plurality of resources;    -   a second cluster consisting of resources having the longest        durations of the plurality of resources; and preferably (though        optionally)    -   a third cluster consisting of resources of the plurality of        resources having durations between the shortest durations and        the longest durations.

After the resources have been clustered based on duration, then in step835 the techniques herein classify the resources according to theircorresponding cluster. That is, illustratively, those resources of thefirst cluster may be classified as cached resources, those resources ofthe second cluster may be classified as non-cached resources, and if soconfigured (e.g., for greater confidence in the first and secondclusters), those resources of the third cluster may be classified asunclassified resources (i.e., uncertain as to either being a cached ornon-cached resource).

As described in greater detail above, in step 840 one or moreclassification-based actions may be performed. As one example, thetechniques herein may determine a proportion of cached resources andnon-cached resources and display the proportion within a web pageperformance dashboard (GUI). In another example, the techniques hereinmay correlate a plurality of snapshots of renderings of the web pagefrom a plurality of client devices, and may generate a report on cachedand non-cached resources across the plurality of snapshots of renderingsof the web page. For instance, different correlations between cached andnon-cached resources may be made herein (e.g., based on particularbusiness transactions, nodes, web browsers, etc.), and also differentvisualizations may be made, such as in a GUI or other reporting datastructure. Other examples, such as dynamically changing caching headersin order to achieve different caching results may also be performed,whether by the controller or an administrator.

In still further embodiments of the techniques herein, a business impactof caching/non-caching can also be quantified. That is, because of theproportion of cached to non-cached resources, various businesstransactions may have been correspondingly affected (e.g., onlinepurchases were delayed, page visits were halted before fully loading,user satisfaction or dwell time decreased, etc.). The techniques herein,therefore, may compare different proportions (e.g., from differentclient devices, different web browsers, etc.) to show any affect (e.g.,value lost, transactions lost, visits missed, revenue difference, etc.)due to the caching behavior of the web page on various client devices.

The simplified example procedure 800 may then end in step 845, notablywith the option to continue monitoring further page loads.

It should be noted that certain steps within procedure 800 may beoptional as described above, and the steps shown in FIG. 8 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

Moreover, the procedure 800 is described from one example perspective(e.g., from the perspective of a server/controller 490), howeverreciprocal actions of participating devices may be understood from thedescription above (e.g., from the perspective of a network browser agent412/419). Also, as mentioned herein, though one example embodimentperforms the clustering and classifying at the server/controller 490,other embodiments (e.g., where suitable processing capability isavailable) may shift the responsibility of various actions to otherdevices/agents, which may then be locally reported and/or reported to acentralized server for further processing and correlation.

The techniques described herein, therefore, provide for resource cacheclassification using machine learning. In particular, the techniquesherein provide valuable insight into browser resource caching, andspecifically whether a resource is fetched from local cache or not,regardless of the web browser used or privacy levels set, and does sowith a high degree of accuracy. For example, in field tests, thetechniques herein have achieved an accuracy of 95% or higher, where lessthan 10% of the resources were placed within the unclassified cluster.The techniques herein thus reliably provide a tool for detecting andreporting inefficiencies in resource caching, which may then be managedby the server/controller or else otherwise attended to by anetwork/application administrator. Such a tool enhances the end-to-endmonitoring capabilities of the example application intelligence platformabove.

While there have been shown and described illustrative embodiments thatprovide for resource cache classification using machine learning, it isto be understood that various other adaptations and modifications may bemade within the spirit and scope of the embodiments herein. For example,while certain embodiments are described herein with respect to certaintypes of networks in particular, the techniques are not limited as suchand may be used with any computer network, generally, in otherembodiments. Moreover, while specific technologies, protocols, andassociated devices have been shown, such as Java, TCP, IP, and so on,other suitable technologies, protocols, and associated devices may beused in accordance with the techniques described above. In addition,while certain devices are shown, and with certain functionality beingperformed on certain devices, other suitable devices and processlocations may be used, accordingly. That is, the embodiments have beenshown and described herein with relation to specific networkconfigurations (orientations, topologies, protocols, terminology,processing locations, etc.). However, the embodiments in their broadersense are not as limited, and may, in fact, be used with other types ofnetworks, protocols, and configurations.

Moreover, while the present disclosure contains many other specifics,these should not be construed as limitations on the scope of anyinvention or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularinventions. Certain features that are described in this document in thecontext of separate embodiments can also be implemented in combinationin a single embodiment. Conversely, various features that are describedin the context of a single embodiment can also be implemented inmultiple embodiments separately or in any suitable sub-combination.Further, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

For instance, while certain aspects of the present disclosure aredescribed in terms of being performed “by a server” or “by acontroller”, those skilled in the art will appreciate that agents of theapplication intelligence platform (e.g., application agents, networkagents, language agents, etc.) may be considered to be extensions of theserver (or controller) operation, and as such, any process stepperformed “by a server” need not be limited to local processing on aspecific server device, unless otherwise specifically noted as such.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in the present disclosure should not be understoodas requiring such separation in all embodiments.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments is herein.

What is claimed is:
 1. A method, comprising: determining, by aprocessor, a plurality of resources loaded during rendering of a webpage on a client device; determining, by the processor, a duration oftime taken for each of the plurality of resources to fully load;clustering, by the processor, the plurality of resources into clusters,comprising: a first cluster consisting of resources having the shortestdurations of the plurality of resources; and a second cluster consistingof resources having the longest durations of the plurality of resources;classifying, by the processor, those resources of the first cluster ascached resources; and classifying, by the processor, those resources ofthe second cluster as non-cached resources.
 2. The method as in claim 1,wherein clustering the plurality of resources into clusters furthercomprises a third cluster consisting of resources of the plurality ofresources having durations between the shortest durations and thelongest durations, the method further comprising: classifying thoseresources of the third cluster as unclassified resources.
 3. The methodas in claim 1, further comprising: determining that the rendering of theweb page is the first time the web page is rendered on the clientdevice; and classifying all of the plurality of resources as non-cachedresources in response to the rendering of the web page being the firsttime the web page is rendered on the client device.
 4. The method as inclaim 3, wherein determining that the rendering of the web page is thefirst time the web page is rendered on the client device is in responseto: determining that a given percentage of the plurality of resourceshave a duration beyond a given threshold.
 5. The method as in claim 4,wherein the given threshold is based on which web browser performs therendering of the web page.
 6. The method as in claim 1, furthercomprising: determining a proportion of cached resources and non-cachedresources; and displaying the proportion within a web page performancedashboard.
 7. The method as in claim 1, further comprising: correlatinga plurality of snapshots of renderings of the web page from a pluralityof client devices; and generating a report on cached and non-cachedresources across the plurality of snapshots of renderings of the webpage.
 8. The method as in claim 1, wherein the plurality of resourcescomprise both same-origin and cross-origin resources.
 9. The method asin claim 1, wherein clustering the plurality of resources into clustersis based on a machine learning algorithm.
 10. A tangible,non-transitory, computer-readable medium storing program instructionsthat cause a computer to execute a process comprising: determining aplurality of resources loaded during rendering of a web page on a clientdevice; determining a duration of time taken for each of the pluralityof resources to fully load; clustering the plurality of resources intoclusters, comprising: a first cluster consisting of resources having theshortest durations of the plurality of resources; and a second clusterconsisting of resources having the longest durations of the plurality ofresources; classifying those resources of the first cluster as cachedresources; and classifying those resources of the second cluster asnon-cached resources.
 11. The computer-readable medium as in claim 10,wherein clustering the plurality of resources into clusters furthercomprises a third cluster consisting of resources of the plurality ofresources having durations between the shortest durations and thelongest durations, the process further comprising: classifying thoseresources of the third cluster as unclassified resources.
 12. Thecomputer-readable medium as in claim 10, the process further comprising:determining that the rendering of the web page is the first time the webpage is rendered on the client device; and classifying all of theplurality of resources as non-cached resources in response to therendering of the web page being the first time the web page is renderedon the client device.
 13. The computer-readable medium as in claim 12,wherein determining that the rendering of the web page is the first timethe web page is rendered on the client device is in response to:determining that a given percentage of the plurality of resources have aduration beyond a given threshold.
 14. The computer-readable medium asin claim 13, wherein the given threshold is based on which web browserperforms the rendering of the web page.
 15. The computer-readable mediumas in claim 10, the process further comprising: determining a proportionof cached resources and non-cached resources; and displaying theproportion within a web page performance dashboard.
 16. Thecomputer-readable medium as in claim 10, the process further comprising:correlating a plurality of snapshots of renderings of the web page froma plurality of client devices; and generating a report on cached andnon-cached resources across the plurality of snapshots of renderings ofthe web page.
 17. The computer-readable medium as in claim 10, whereinthe plurality of resources comprise both same-origin and cross-originresources.
 18. The computer-readable medium as in claim 10, whereinclustering the plurality of resources into clusters is based on amachine learning algorithm.
 19. An apparatus, comprising: one or morenetwork interfaces configured to communicate in a computer network; aprocessor coupled to the network interfaces and adapted to execute oneor more processes; and a memory configured to store a process executableby the processor, the process when executed operable to: determine aplurality of resources loaded during rendering of a web page on a clientdevice; determine a duration of time taken for each of the plurality ofresources to fully load; cluster the plurality of resources intoclusters, comprising: a first cluster consisting of resources having theshortest durations of the plurality of resources; and a second clusterconsisting of resources having the longest durations of the plurality ofresources; classify those resources of the first cluster as cachedresources; and classify those resources of the second cluster asnon-cached resources.
 20. The apparatus as in claim 19, whereinclustering the plurality of resources into clusters further comprises athird cluster consisting of resources of the plurality of resourceshaving durations between the shortest durations and the longestdurations, wherein the process when executed is further operable to:classify those resources of the third cluster as unclassified resources.21. The apparatus as in claim 19, wherein the process when executed isfurther operable to: determine that the rendering of the web page is thefirst time the web page is rendered on the client device; and classifyall of the plurality of resources as non-cached resources in response tothe rendering of the web page being the first time the web page isrendered on the client device.